U.S. patent application number 10/817644 was filed with the patent office on 2005-02-24 for cellular analysis.
Invention is credited to Hashemi, Brian.
Application Number | 20050042688 10/817644 |
Document ID | / |
Family ID | 33097503 |
Filed Date | 2005-02-24 |
United States Patent
Application |
20050042688 |
Kind Code |
A1 |
Hashemi, Brian |
February 24, 2005 |
Cellular analysis
Abstract
The present invention relates to the analysis of cells, their
cytoskeletal protein, and uses thereof. More particularly, the
present invention relates to methods of analyzing cytoskeletal
protein for a range of applications including, methods of measuring
cellular responses and methods of identifying biomolecular
signatures.
Inventors: |
Hashemi, Brian; (Houston,
TX) |
Correspondence
Address: |
WOODCOCK WASHBURN LLP
ONE LIBERTY PLACE, 46TH FLOOR
1650 MARKET STREET
PHILADELPHIA
PA
19103
US
|
Family ID: |
33097503 |
Appl. No.: |
10/817644 |
Filed: |
April 2, 2004 |
Related U.S. Patent Documents
|
|
|
|
|
|
Application
Number |
Filing Date |
Patent Number |
|
|
10817644 |
Apr 2, 2004 |
|
|
|
10407262 |
Apr 4, 2003 |
|
|
|
Current U.S.
Class: |
435/7.2 |
Current CPC
Class: |
G01N 2333/4712 20130101;
G01N 33/56972 20130101; G01N 2800/52 20130101 |
Class at
Publication: |
435/007.2 |
International
Class: |
G01N 033/53 |
Goverment Interests
[0002] Portions of the disclosure herein may have been supported in
part by a grant from the National Aeronautics and Space
Administration, Grant No. NAG 2-1357. The United States Government
may have certain rights in this application.
Claims
What is claimed:
1. A method for measuring a cellular response comprising: i.
stabilizing a mixture of cells comprising a plurality of cell
types; ii. labeling two or more cell types from the mixture using
cell type-specific reagent; and iii. assessing the content of
cytoskeletal protein associated with the two or more cell
types.
2. The method of claim 1 further comprising a step of: iv.
comparing the content of the cytoskeletal protein associated with
the two or more cell types with the content of cytoskeletal protein
associated with corresponding cell types from a control.
3. The method of claim 1 further comprising a step of determining
the size and granularity of the two or more cell types.
4. The method of claim 3 further comprising a step of comparing the
content of the cytoskeletal protein associated with the two or more
cell types, the cell size, and the cell granularity of the two or
more cell types with a content of cytoskeletal protein, cell size,
and cell granularity in corresponding cell types from a
control.
5. The method of claim 1 wherein the two or more cell types
comprise at least one of an immune cell.
6. The method of claim 1 wherein the two or more cell types
comprise at least one of a lymphocyte, neutrophil, monocyte,
eosinophil, erythrocyte, platelet, or basophil.
7. The method of claim 1 wherein the cytoskeletal protein is
F-actin.
8. The method of claim 1 wherein the mixture of cells is collected
using a non-chelating anticoagulant.
9. The method of claim 1 wherein the cells are stabilized at a
temperature of from about 27 to about 50 degrees Celsius.
10. The method of claim 1 wherein the cells are stabilized at
physiological temperature.
11. The method of claim 1 wherein assessing the content of the
cytoskeletal protein is performed using a flow cytometer.
12. The method of claim 1 further comprising the step of labeling
cytoskeletal protein associated with the two or more cell
types.
13. The method of claim 12 wherein assessing the content of the
cytoskeletal protein is performed by microscopy.
14. The method of claim 1 wherein the cells are stabilized by
fixation.
15. The method of claim 1 further comprising a step of providing a
biologically active agent to the mixture of cells before
stabilizing the cells.
16. The method of claim 15 wherein the biologically active agent is
a stimulant or a depressant.
17. The method of claim 15 wherein the agent is a toxin.
18. The method of claim 15 wherein the agent is a bacterial or
viral toxin.
19. The method of claim 15 wherein the agent is a drug or a small
molecule.
20. The method of claim 19 wherein the agent is an enzyme
regulator, immune modulator, or chemotherapeutic agent.
21. A method for measuring a cellular response comprising: i.
assessing the content of cytoskeletal protein associated with a
plurality of cell types; and ii. comparing the content of the
cytoskeletal protein associated with said plurality of cell types
to the content of corresponding cytoskeletal protein associated
with corresponding cell types from a control.
22. The method of claim 21 further comprising a step of determining
the size and granularity of the multiple cell types.
23. The method of claim 22 further comprising a step of comparing
the content of the cytoskeletal protein associated with the
plurality of cell types, the cell size, and the cell granularity of
the plurality of cell types with a content of cytoskeletal protein,
cell size, and cell granularity in corresponding cell types from a
control.
24. The method of claim 21 wherein the plurality of cell types
comprise immune cells.
25. The method of claim 21 wherein the plurality of cell types
comprise at least one of a lymphocyte, neutrophil, monocyte,
eosinophil, erythrocyte, platelet, or basophil.
26. The method of claim 21 wherein the cytoskeletal protein is
F-actin.
27. The method of claim 21 wherein assessing the content of the
cytoskeletal protein is performed using a flow cytometer.
28. The method of claim 21 further comprising a step of providing a
biologically active agent to the plurality of cell types before
assessing the content of cytoskeletal protein.
29. The method of claim 28 wherein the biologically active agent is
a stimulant or a depressant.
30. The method of claim 28 wherein the agent is a toxin.
31. The method of claim 28 wherein the agent is a bacterial or
viral toxin.
32. The method of claim 15 wherein the agent is a drug or a small
molecule.
33. The method of claim 32 wherein the agent is an enzyme
regulator, immune modulator, or chemotherapeutic agent.
34. A method for measuring a cellular response comprising: i.
stabilizing a mixture of cells comprising a plurality of cell
types; and ii. assessing the content of cytoskeletal protein
associated with two or more cell types.
35. A method for measuring a cellular response comprising: i.
stabilizing a mixture of cells comprising one cell type or a
plurality of cell types at a temperature of from about 27 to about
50 degrees Celsius; and ii. assessing the content of cytoskeletal
protein associated with the one or more cell types.
36. The method of claim 35 wherein the temperature is from about 30
to about 40 degrees Celsius.
37. A method for identifying a cytoskeletal signature comprising
the step of: i. assessing the content of cytoskeletal protein
associated with a plurality of cell types.
38. The method of claim 37 further comprising the step of ii.
comparing the content of the cytoskeletal protein associated with
said plurality of cell types to the content of corresponding
cytoskeletal protein associated with corresponding cell types from
a control.
39. The method of claim 37 further comprising a step of determining
the size and granularity of the plurality of cell types.
40. The method of claim 39 further comprising a step of comparing
the content of the cytoskeletal protein associated with the
plurality of cell types, the cell size, and the cell granularity of
the plurality of cell types with a content of cytoskeletal protein,
cell size, and cell granularity in corresponding cell types from a
control.
41. The method of claim 37 wherein the plurality of cell types
comprise at least one of a lymphocyte, neutrophil, monocyte,
eosinophil, erythrocyte, platelet, or basophil.
42. The method of claim 37 wherein the plurality of cell types
comprise immune cells.
43. The method of claim 37 wherein the cytoskeletal protein is
F-actin.
44. The method of claim 37 wherein assessing the content of the
cytoskeletal protein is performed using a flow cytometer.
45. The method of claim 37 further comprising a step of providing a
biologically active agent to the plurality of cell types before
assessing the content of cytoskeletal protein.
46. The method of claim 45 wherein the biologically active agent is
a stimulant or a depressant.
47. The method of claim 45 wherein the agent is a toxin.
48. The method of claim 45 wherein the agent is a bacterial or
viral toxin.
49. The method of claim 15 wherein the agent is a drug or a small
molecule.
50. The method of claim 49 wherein the agent is an enzyme
regulator, immune modulator, or chemotherapeutic agent.
51. A method for assessing the presence or absence of a disease
state in a subject comprising: i. assessing the content of
cytoskeletal protein associated with a plurality of cell types from
the subject; ii. correlating the content with the presence or
absence of a disease state in the subject.
52. The method of claim 51 wherein said correlating step is
performed by comparing the content of cytoskeletal protein
associated with said plurality of cell types to the content of
corresponding cytoskeletal protein associated with corresponding
cell types from a control.
53. The method of claim 51 further comprising a step of determining
the size and granularity of the plurality of cell types.
54. The method of claim 53 wherein said correlating step is
performed by comparing the content of the cytoskeletal protein
associated with the plurality of cell types, the cell size and the
cell granularity of the plurality of cell types with a content of
cytoskeletal protein, cell size, and cell granularity in
corresponding cell types from a control.
55. The method of claim 51 wherein the plurality of cell types
comprise at least one of a lymphocyte, neutrophil, monocyte,
eosinophil, erythrocyte, platelet, or basophil.
56. The method of claim 51 wherein the plurality of cell types
comprise immune cells.
57. The method of claim 51 wherein the cytoskeletal protein is
F-actin.
58. The method of claim 51 wherein assessing the content of the
cytoskeletal protein is performed using a flow cytometer.
59. The method of claim 51 wherein the disease state is bacterial
infection.
60. The method of claim 51 wherein the disease state is viral
infection.
61. The method of claim 51 wherein the disease state is cancer.
62. The method of claim 51 wherein the disease state is exposure to
biological or chemical agent.
63. A method for measuring a clinical parameter in a subject
comprising: i. assessing the content of cytoskeletal protein
associated with a plurality of cell types from each of a plurality
of subjects belonging to a least two population groups differing
with respect to at least one clinical parameter associated with a
disease state; ii. comparing the content of corresponding
cytoskeletal protein associated with said plurality of cell types
from said groups to each other to create cytoskeletal protein
profiles that are associated with the clinical parameter.
64. A method for determining a response profile to a drug
comprising i. assessing the content of cytoskeletal protein
associated with a plurality of cell types that have been exposed to
the drug; and ii. correlating the content of cytoskeletal protein
with a probability of being a positive responder, negative
responder, or non-responder to therapy with said drug.
65. The method of claim 64 wherein said correlating step is
performed by comparing the content of cytoskeletal protein
associated with said plurality of cell types to the content of
corresponding cytoskeletal protein in corresponding cell types from
a control.
66. The method of claim 64 further comprising a step of determining
the size and granularity of the plurality of cell types.
67. The method of claim 66 wherein said correlating step is
performed by comparing the content of the cytoskeletal protein
associated with the plurality of cell types, the cell size and the
cell granularity of the plurality of cell types with a content of
cytoskeletal protein, cell size, and cell granularity in
corresponding cell types from a control.
68. A method for monitoring the progression of a disease state in a
subject comprising: i. assessing the content of cytoskeletal
protein associated with a plurality of cell types from the subject;
ii. correlating the content of cytoskeletal protein with
progression of the disease state in the subject.
69. The method of claim 68 wherein said correlating step is
performed by comparing the content of cytoskeletal protein
associated with said plurality of cell types to the content of
corresponding cytoskeletal protein in corresponding cell types from
a control.
70. The method of claim 68 further comprising a step of determining
the size and granularity of the plurality of cell types.
71. The method of claim 70 wherein said correlating step is
performed by comparing the content of the cytoskeletal protein
associated with the plurality of cell types, the cell size and the
cell granularity of the plurality of cell types with a content of
cytoskeletal protein, cell size, and cell granularity in
corresponding cell types from a control.
72. The method of claim 68 further comprising a step of providing a
biologically active agent to the plurality of cell types before
assessing the content of cytoskeletal protein.
73. A method for determining donor-recipient compatibility for
transplant therapy comprising: i. assessing the content of
cytoskeletal protein associated with a plurality of cell types from
the recipient; ii. correlating the content of cytoskeletal protein
with compatibility to the transplant.
74. The method of claim 73 further comprising a step of determining
the size and granularity of the plurality of cell types.
75. A method of generating a classification system for classifying
a cell sample: i. providing a learning set comprising a plurality
of data objects, wherein each data object comprises data
representing measurements of cytoskeletal protein in sample, and
wherein the samples are classified according to at least two
different clinical parameters; and ii. generating a classification
model, wherein the classification model classifies a cell sample as
indicative of a clinical parameter, indication, or condition.
76. A method for measuring the content of cytoskeletal protein
comprising: i. stabilizing a mixture of cells comprising a
plurality of cell types; and ii. assessing the content of
cytoskeletal protein associated with two or more of the cell
types.
77. A method for preserving a cell comprising stabilizing a mixture
of cells comprising one cell type or a plurality of cell types at a
temperature of from about 27 to about 50 degrees Celsius.
78. The method of claim 77, wherein the temperature is from about
30 to about 40 degrees Celsius.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application is a Continuation-In-Part of U.S.
application Ser. No. 10/407,262 filed Apr. 4, 2003, which is
incorporated herein by reference in its entirety.
FIELD
[0003] The present invention relates to the analysis of cells,
their cytoskeletal protein, and uses thereof. More particularly,
the present invention relates to methods of analyzing cytoskeletal
protein for a range of applications including, methods of measuring
cellular responses and methods of identifying biomolecular
signatures.
BACKGROUND
[0004] Cells contain an intricate network of protein filaments that
extend throughout the cytoplasm called the cytoskeleton. The
cytoskeleton is a highly dynamic structure that reorganizes
continuously in response to various internal and external stimuli
and provides cells with the ability to adopt different shapes and
carry out coordinated and directed movements. The cytoskeleton
plays a crucial role in signal transduction and functional
responses of all human cells (Rozdzial et al., Immunity 1995, 3:
623-633; Gomez, et al. Eur. J. Immunol. 1995, 25: 2673-2678) and is
involved in many other aspects of cellular function including
orchestration of mechanical forces inside cells (Bunnell et al.
Immunity 2001, 14: 315-329; Goebel, J. Transplant. Proc. 1999, 31:
822-824).
BRIEF DESCRIPTION OF THE DRAWINGS
[0005] FIG. 1: shows that cellular F-actin contents are sensitive
to temperature. In this example, purified T cells were incubated at
the indicated temperatures (4.degree. C., room temperature
(25.degree. C.), and 37.degree. C., respectively) for approximately
30 minutes. Cells were then fixed at the indicated temperatures and
labeled with a fluorescent F-actin probe. The relative F-actin
contents were measured by flow cytometry.
[0006] FIG. 2: shows the effect of calcium ion concentrations on
the inducibility of actin polymerization in T cells. In this
example, whole blood was collected from a donor in collection tubes
containing heparin (curve 1) or EDTA (curve 2), respectively, as
the anticoagulants. Blood samples were then incubated at 37.degree.
C. and activated with phorbol ester and a calcium ionophore
(PDBu/I, phorbol-12,13-dibutyrate/ionomycin- ). Chelation of
calcium by EDTA results in a dramatic decrease in the
responsiveness of T-cells as evidenced by the lower level of
inducible actin polymerization by the PDBu/I.
[0007] FIG. 3: shows the difference in background F-actin contents
in Jurkat T cells as a function of centrifugal force and shows that
activation-induced polymerization of actin is sensitive to
centrifugal force and is dramatically reduced in Jurkat T cells
following exposure to 300 g. RCF refers to relative centrifugal
force.
[0008] FIGS. 4A-F: Activation of whole blood cultures with an
Activator Cocktail of final concentration of 30 .mu.g/ml OKT3 for
activation of T-cells, 10.sup.-6 M FMLP for activation of
neutrophils, and 100 .mu.g/ml LPS for activation of monocytes was
performed for 90 seconds at 37.degree. C. followed by fixation and
labeling of F-actin by Bodippy Phallacidin and surface makers for
identification of each cell type. Data were collected on a
FacsCaliber instrument to assess the ability of cells to polymerize
F-actin in response to receptor mediated stimulation. FIG. 4A:
shows the forward scatter vs. side scatter plot and the gating used
to identify neutrophils; FIG. 4B: shows the gating parameter used
to identify T-cells; FIG. 4C: shows the gating parameter used to
identify monocytes. FIGS. 4D-4F: shows the histogram plot for the
actin content of each cell type. The relative F-actin content of
each cell was measured using the actin channel and the fluorescence
level of each cell is displayed on the corresponding histogram of
each cell type; neutrophil F-actin (4D), T-cell F-actin (4E), and
monocyte F-actin (4F). In this manner, the relative mean
fluorescence associated with the F-actin content of each cell
population was calculated using statistical analysis of the
data.
[0009] FIG. 5A-C: Dose response curves for activation of actin
polymerization in whole blood samples shows the relative F-actin
levels at every concentration of stimulant (NA=neutrophil F-actin;
TA=T-cells F-actin; MA=monocyte F-actin). FIG. 5A: Whole blood
samples were stimulated for 90 seconds with LPS. FIG. 5B: Whole
blood samples were stimulated for 90 seconds with FMLP. FIG. 5C:
Whole blood samples were stimulated for 90 seconds with OKT3. The
error bars represent the Standard Error of Mean for duplicate
samples.
[0010] FIG. 6A: Stimulation of whole blood cultures with Activator
Cocktail containing 30 .mu.g/ml OKT3, 10.sup.-7M FMLP, and 100
.mu.g/ml LPS at 37.degree. C. activates polymerization of F-actin
in neutrophil, monocyte, and T-cell populations. The time-course of
activation indicates an optimum activation time of approximately 90
seconds for stimulation of whole blood cultures and measurement of
the responsiveness of cells. FIG. 6B: F-actin levels (-) and
responses to activator cocktail (+) were measured using blood
samples from 6 healthy adult donors. Blood specimens were obtained
at 3 time points over a two week period for all donors (total of 18
samples) and they were analyzed by the present methods.
[0011] FIGS. 7A-B: 7A: Infection of whole blood cultures with live
Salmonella typhimurium results in dramatic inhibition of leukocyte
response to receptor-mediated activation. Blood cultures were
incubated with 108 bacterial cells/ml for the indicated amount of
time. Samples were then stimulated with activator cocktail
containing 30 .mu.g/ml OKT3, 10.sup.-7M FMLP, and 100 .mu.g/ml LPS
for 90 seconds at 37.degree. C. F-actin levels were measured using
the present methods and the increase in F-actin was calculated as
percent activation relative to unstimulated control. (In this case
the unstimulated control is blood samples that are infected with
Salmonella for the same amount of time.) FIG. 7B: shows the use of
cellular parameters such as population mean of F-actin, Forward
Scatter, and Side Scatter for neutrophils, T-cells, and monocytes
to generate a signature associated with Salmonella infection. As
Salmonella infects leukocytes in whole blood cultures it imparts
unique changes in the signature which is characterized by changes
in signature parameters such as F-actin, Forward Scatter, and Side
Scatter of neutrophils, monocytes and T-cells, as well as their
responses to receptor-mediated stimulation. Salmonella infection
can alter some signature parameters dramatically (up arrows) but
has no effect on some of the other signature parameters (down
arrows). (NA=neutrophil actin; NFS=neutrophil forward scatter;
NSS=neutrophil side scatter; TA=T-cell actin; TFS=T-cell forward
scatter; TSS=T-cell side scatter; MA=monocyte actin; MFS=monocyte
forward scatter; MSS=monocyte side scatter. (+) indicates
parameters associated with samples that were treated with activator
cocktail and (-) indicates parameters associated with samples not
treated with activator cocktail.)
[0012] FIG. 8A-C: Biomolecular signatures of whole blood cultures
infected with a variety of bacterial cells. FIG. 8A demonstrates
that members of the Staphlococcus genus produce unique yet similar
signatures. FIG. 8B demonstrates that gram negative Salmonella and
gram positive Staphlococcus epidermis produce unique and different
signatures. FIG. 8C demonstrates that two gram negative organisms
from different genus Salmonella and E. Coli exhibit different
signatures. (NA=neutrophil actin; NFS=neutrophil forward scatter;
NSS=neutrophil side scatter; TA=T-cell actin; TFS=T-cell forward
scatter; TSS=T-cell side scatter; MA=monocyte actin; MFS=monocyte
forward scatter; MSS=monocyte side scatter. (+) indicates
parameters associated with samples that were treated with activator
cocktail for 90 seconds, and (-) indicates parameters associated
with samples not treated with activator cocktail.)
[0013] FIG. 9: FIG. 9A demonstrates the evolution of biomolecular
signatures for the infection of whole blood cultures with E. Coli
during the first 90 minutes of infection. FIG. 9B shows a Radargram
for the signatures in 9A providing a graphical display of the
unique signature of E. Coli. (NA=neutrophil actin; NFS=neutrophil
forward scatter; NSS=neutrophil side scatter; TA=T-cell actin;
TFS=T-cell forward scatter; TSS=T-cell side scatter; MA=monocyte
actin; MFS=monocyte forward scatter; MSS=monocyte side scatter. (+)
indicates parameters associated with samples that were treated with
activator cocktail, and (-) indicates parameters associated with
samples not treated with activator cocktail.)
[0014] FIGS. 10A-C: FIG. 10A demonstrates the biomolecular
signatures after infection of whole blood for ten minutes with
select live bacteria. FIG. 10B demonstrates the biomolecular
signatures after infection of whole blood for thirty minutes with
select live bacteria. FIG. 10C demonstrates the biomolecular
signatures after infection of whole blood for ninety minutes with
select live bacteria. (NA=neutrophil actin; NFS=neutrophil forward
scatter; NSS=neutrophil side scatter; TA=T-cell actin; TFS=T-cell
forward scatter; TSS=T-cell side scatter; MA=monocyte actin;
MFS=monocyte forward scatter; MSS=monocyte side scatter. (+)
indicates parameters associated with samples that were treated with
activator cocktail and (-) indicates parameters associated with
samples not treated with activator cocktail.)
SUMMARY
[0015] This invention relates, in part, to the discovery that by
measuring changes in select biophysical properties of cells, the
classification of cellular responses is made possible. Thus, the
present invention provides methods of measuring changes in certain
biophysical properties of cells, such as, for example, changes in
the content of cytoskeletal protein in the cell, cell size, and
cell granularity at different times during a cell's lifecycle and
in response to a variety of biologically active agents. The present
methodology permits the profiling of mammalian subjects based on
the biophysical properties of their cells, and in particular, based
on cellular signatures.
[0016] The present invention provides methods of identifying and
using cytoskeletal signatures. As used herein, the terms
"cytoskeletal signature" or "cellular cytoskeletal signature"
refers to the content of cytoskeletal protein associated with a
cell. Accordingly, a "cytoskeleton signature" or "cellular
cytoskeleton signature" can be identified by assessing the content
of cytoskeletal protein associated with one or more cell types. For
use herein, cytoskeleton protein that is associated with a cell is
cytoskeleton protein that is in the cell or on the surface of the
cell.
[0017] The actin cytoskeleton exists in two states: monomeric or
G-actin, and its polymerized state known as F-actin (fillamentous
actin). Mammalian cells rely on the polymerization and
de-polymerization of actin for many cellular processes. In some
embodiments of the present invention, the actin cytoskeletal
signature of a cell is identified. An actin signature can be
identified, for example, by assessing the content of F-actin or
G-actin associated with one or more cells at a certain time
point.
[0018] Some embodiments of the present invention include a step of
assessing the content of cytoskeletal protein associated with one
or more cell types. As used herein, the term "assessing the
content" can refer to determining, detecting, measuring, or
quantifying the total quantity or relative quantity of one or more
types of cytoskeletal protein associated with the one or more cell
types. In some embodiments, "assessing the content" of cytoskeleton
protein" is performed by determining the polymerization state of a
certain type of cytoskeletal protein in a cell. For example, in
some embodiments, "assessing the content of the cytoskeleton
protein" refers to determining, detecting, measuring, or
quantifying the amount of a certain type of polymerized
cytoskeletal protein or unpolymerized cytoseketal protein in a
cell. For example, it can refer to determining, detecting,
measuring, or quantifying the amount of F-actin or G-actin in a
cell.
[0019] There are many techniques for measuring cytoskeletal protein
associated with a cell. All of these techniques can be used in
accordance with the present invention. In accordance with some
particular embodiments, the present invention provides methods for
measuring cellular responses in a subject or measuring the
cytoskeletal content associated with a cell comprising (i)
stabilizing a mixture of cells, (ii) labeling one or more cell
types from the mixture using cell type-specific reagent, and (iii)
assessing the content of cytoskeletal protein associated with the
one or more cell types. The present invention also provides methods
for measuring cellular responses in a subject or measuring the
cytoskeletal content associated with a cell that do not require the
step of labeling one or more cell types from the mixture. In one
aspect, the cells are stabilized at a temperature of from about 27
degrees Celsius to about 50 degrees Celsius, with a temperature of
from about 30 to about 40 degrees Celsius, and in particular, a
physiological temperature, i.e., a temperature of about 37 degrees
Celsius, being preferred for some uses. Additional temperatures,
for example, temperatures from about 4 degrees Celsius to about 50
degrees, or 25 degrees Celsius to about 40 degrees are expressly
included within the scope of the present invention.
[0020] Any method of stabilizing cells can be used in accordance
with the present invention. For example, the cells can be
stabilized by fixation. In some aspects of the present invention,
the cells are collected from a subject using a non-chelating
coagulant.
[0021] Cytoskeletal protein can be assessed using any known
technique to detect and/or measure cytoskeletal protein content
including cytoskeleton polymerization states. For example, in
accordance with some particular embodiments, the cytoskeletal
protein is first labeled and microscopy techniques, such as
fluorescence microscopy techniques, or flow cytometry techniques
are used to assess cytoskeletal protein content. It is not always
necessary to label the cytoskeletal protein before assessing the
cytoskeletal protein content.
[0022] For the purposes of the present application, the term
"cytoskeletal protein" or "cellular cytoskeletal protein" refers to
any subset of a cytoskeletal protein, for example, cytoskeletal
protein can refer to F-actin, G-actin, or total actin. The
cytoskeletal protein that is assessed (e.g., detected, quantified,
or measured) can be any cytoskeletal protein type including, for
example, actin microfilaments, intermediate filaments,
microtubules, spectrin, talin, vinculin, desmin, senaptin,
vimentin, ezrin, moesin, filamin, phakinin, actinin, profilin,
fibrin, keratin, myosin, dynein, and kinesin. In some embodiments,
only one type of cytoskeletal protein type is assessed, e.g., only
F-actin or only G-actin. In other embodiments, more than one type
of cytoskeletal protein can be assessed e.g., F-actin and
senaptin.
[0023] The present invention includes methods for identifying the
cytoskeletal signature of a cytoskeletal protein comprising a step
of assessing the content of cytoskeletal protein in or on the
surface of one cell type or a plurality of cell types. In some
embodiments, the methods further comprise a step of comparing the
content of cytoskeletal protein associated with the one cell type
or plurality of cell types to the content of corresponding
cytoskeletal protein associated with corresponding cell types from
a control. By assessing the content of cytoskeletal protein
associated with a cell using the methods described herein, it is
possible, inter alia, to determine the polymerization state of
cytoskeletal protein in a cell at a certain time point.
[0024] In various embodiments of the present invention, one will be
comparing the content of cytoskeletal protein and/or other cellular
parameters associated with one cell type or a plurality of cell
types to the content of corresponding cytoskeletal protein and/or
other cellular parameters associated with corresponding cell types
from a control. In other words, one will be comparing cytoskeletal
signatures or biomolecular signatures in one sample comprising a
mixture of cells to another sample comprising a mixture of cells.
For purposes of this application, when two signatures are being
compared, one signature can act as a control for another. For
example, in comparing infection by a strain of Salmonella to
infection by a strain of E. Coli, one of the signatures can act as
a control for the other for the purposes of this application. Other
examples include comparing the cytoskeletal or biomolecular
signature of a blood sample from a patient with the cytoskeletal or
biomolecular signature of blood samples from a healthy donor, or a
group of healthy donors, in which case the healthy donor signatures
serve as a control for the signature of the patient blood sample.
In another example, blood samples from a patient can be exposed to
a number of different drugs to compare the cytoskeletal or
biomolecular signature of the patient blood sample after exposure
to the drug. In this example, the biomolecular signature or
cytoskeletal signature of one drug acts as a control for comparison
with the biomolecular signature or cytoskeletal signature of
another drug.
[0025] In some embodiments of the present invention, it will be
desirable to measure not only cytoskeleton signature but additional
cellular properties or parameters including, for example, cell
size, cell granularity, number of receptors on the surface of a
cell, number of cells in a biological sample, uptake of specific
dye such as lipids dyes or nucleic acid dyes, and the like. The
term "biomolecular signature" as used herein refers to the
cytoskeleton signature in a cell as well as one or more additional
cellular parameters.
[0026] All living creatures are made of cells. Eukaryotic cells
contain a large quantity of DNA and an array of internal membranes.
The cellular cytoskeleton helps organize the cell by keeping
internal cellular structures in their proper place and controlling
their movements. The cytoskeleton is comprised of a networks of
actin microfilaments, intermediate filaments, microtubules and
their related proteins. The polymerization and de-polymerization of
cytoskeletal proteins is said to drive many of the cellular
processes in human cells. The present inventor has discovered that
by assessing the cytoskeletal protein associated with a cell, it is
possible to measure and/or classify cellular responses. Methods for
classifying and/or measuring cellular responses are, accordingly,
encompassed by the present invention. In accordance with some
particular embodiments, these methods comprise (i) assessing the
content of cytoskeletal protein associated with one cell type or a
plurality of cell types and (ii) comparing the content of the
cytoskeletal protein associated with the one cell type or plurality
of cell types to the content of corresponding cytoskeletal protein
associated with corresponding cell types from a control.
[0027] Many human disorders are associated with abnormalities in
the cytoskeletal proteins. For example aggregates of neurofilament
proteins and aberrant accumulation of neurofilaments in motor
neuron cell bodies are associated with several neurological
disorders including amyotrophic lateral sclerosis, infantile spinal
muscular atrophy, and hereditary sensory motor neuropathy.
Neutrophil actin dysfunction has long been recognized as a cause
for poor neutrophil motility, adherence, and phagocytosis in an
infant with life-threatening infections (Boxer et al., N. Engl. J.
Med. 1974, 291: 1093-1099). Studies have revealed an inherited
genetic alteration as the cause of leukocyte actin dysfunction in
some cases and an acquired leukocyte actin dysfunction in many
other clinical conditions (Englich et al., Clin. Infect. Dis. 2001,
33: 2040-2048.).
[0028] Cytoskeletal proteins in or on the surface of a cell are
affected by exposure to both endogenous and exogenous
biologically-active agents. The present inventor has discovered
that the cytoskeletal signature of cells are in flux and that cells
display a unique cytoskeletal signature depending on their internal
and external surroundings. Moreover, it has been discovered that
cytoskeletal signatures can provide information regarding the
status of a cell. For example, a cell that has been exposed to a
certain species of a gram negative bacteria will present a unique
cytoskeletal signature that is indicative of the cell's exposure to
that certain species of gram negative bacteria. Similarly, a cell
that is cancerous will present a unique cytoskeletal signature that
is indicative of the cell's cancerous state. By recognizing the
unique cytoskeletal signatures within a cell, it is possible to,
among other things, assess the presence or absence of disease
states, determine cellular response patterns to different
biologically active agents such as drugs, monitor the progression
of disease states in a subject, and determine donor-recipient
compatibility for transplant therapy. Accordingly, the present
invention provides methods for assessing the presence or absence of
a disease state in a subject comprising (i) assessing the content
of cytoskeletal protein associated with one cell type or a
plurality of cell types from the subject and (ii) correlating the
content with the presence or absence of a disease state in the
subject. The present invention also provides methods for
determining a response profile to a drug comprising (i) assessing
the content of cytoskeletal protein associated with one cell type
or a plurality of cell types from the subject and (ii) correlating
the content of cytoskeletal protein with a probability of being a
positive-responder, negative-responder, or non-responder to therapy
with the drug.
[0029] For use herein, a positive responder, is a subject who
positively responds to treatment, i.e., a subject who experiences
success in amelioration of an injury, pathology, or condition,
including any objective or subjective parameter such as abatement;
remission; diminishing of symptoms or making the injury, pathology,
or condition more tolerable to the patient; slowing in the rate of
degeneration or decline; making the final point of degeneration
less debilitating; or improving a subject's physical or mental
well-being. A positive responder is one in which any toxic or
detrimental side effects of the biologically active agent is
outweighed in clinical terms by therapeutically beneficial effects.
In contrast, a negative responder is one in which the
therapeutically beneficial effects of the treatment is outweighed
by the toxic or detrimental side effects of the biologically active
agent. A non-responder is a subject who doesn't respond to the
treatment or doesn't respond to a satisfactory level.
[0030] The present invention also provides methods for monitoring
the progression of a disease state comprising (i) assessing the
content of cytoskeletal protein associated with one cell type or a
plurality of cell types from the subject and (ii) correlating the
content of cytoskeletal protein with progression of the disease
state in the subject.
[0031] Methods of determining donor-recipient compatibility for
transplant therapy are also encompassed by the present invention.
For example, a blood sample from a recipient will be exposed to a
tissue from the donor and the cytoskeletal or biomolecular
signature of the blood sample will be used to predict likelihood of
rejection or acceptance. In another example, the cytoskeletal or
biomolecular signature of a blood sample from a recipient after
transplant operation will be used to assess the rejection or
acceptance status of the patient. In this example the methods
comprise the steps of (i) assessing the content of cytoskeletal
protein associated with one cell type or a plurality of cell types
from the recipient and (ii) correlating the content of cytoskeletal
protein with compatibility to the transplant. Comparison of
signatures from the recipient to signatures from other patients
that have experienced rejection of a transplant will enable early
detection of rejection in the recipient. In this example,
signatures from patients who have experienced a rejection can serve
as a control for the signature of the recipient.
[0032] Any mammalian cell type can be used in the present
invention. The cells can be selected from a variety of tissue types
including, for example, hematopoietic cells, stem cells, hepatic
cells, muscle cells, nerve cells, mesenchymal cells, cartilage
and/or bone cells, intestinal cells, pancreatic cells or kidney
cells. Cell types include, for example, common lymphoid progenitor
cells, T cells (e.g., helper, cytotoxic, and suppressor cells), B
cells, plasma cells, natural killer cells, common
myeloid-progenitor cells, monocytes, macrophages, mast cells,
leukocytes, basophils, neutrophils, eosinophils, megakaryocytes,
erythrocytes, and cell fragments such as platelets. The term "stem
cell" refers to an undifferentiated cell which is capable of
self-renewal, i.e., proliferation to give rise to more stem cells,
and may give rise to lineage committed progenitors which are
capable of differentiation and expansion into a specific lineage.
As used herein, the term "stem cells" refers generally to
embryonic, hematopoietic and other stem cells of mammalian, e.g.,
human, origin.
[0033] In one preferred embodiment, the cell will be any cell of
the blood and immune system, e.g., erythrocytes, megakaryocytes,
macrophages and related cells such as, for example, monocytes,
connective-tissue macrophages, Langerhans cells, osteoclast cells,
dendritic cells, microglial cells, neutrophils, eosinophils,
basophils, mast cells, T lymphocytes, such as, for example, helper
T cells, suppressor T cells, and killer T cells, B lymphocytes,
such as, for example, IgM, IgG, IgA, IgE, killer cells, and stem
cells and committed progenitors for the blood and immune systems.
In a particularly preferred embodiment, the cells comprise
circulating blood cells such as lymphocytes, neutrophils,
monocytes, eosinophils, red blood cells, platelets, and
basophils.
[0034] The cells can be from any biological sample obtained from
the subject. For example, in some embodiments, the biological
sample will be blood and therefore the mixture of cells will
comprise circulating blood cells. The present invention therefore
describes a method by which the cytoskeletal protein content of
circulating blood cells is assessed. In some embodiments, the
biological sample will be a biopsy sample of a selected tissue in
the body. Non-limiting examples of tissues include tissues from the
liver, lung, heart, breast, and muscle. In some embodiments, the
selected tissue will be diseased, e.g., cancerous. In order to
evaluate the effect of a biologically active agent on cellular
cytoskeletal signature, a biologically active agent can be provided
to the mixture of cells before the cytoskeletal protein is
assessed. In some embodiments, the biologically active agent will
be a stimulant or a depressant. In one aspect, the agent is a
toxin, such as for example, a bacterial or viral toxin. In another
aspect, the agent is a drug or a small molecule. In some
embodiments, the agent is an enzyme regulator, an immune modulator
or a chemotherapeutic agent.
[0035] In accordance with the present invention, the present
methods can further comprise a step of comparing the content of
cytoskeletal protein associated with one or more cell types with
the content of cytoskeletal protein associated with corresponding
cell types from a control. In some embodiments, additional cellular
parameters such as the size and granularity of one or more cell
types are determined. In one embodiment, the size and granularity
is determined by measuring the forward scatter and side scatter of
the cells and correlating forward scatter and side scatter to cell
size and granularity. Additional cellular parameters in one or more
cell types can also be compared to corresponding cellular
parameters in a control.
[0036] One skilled in the art would appreciate that comparing the
content of the cytoskeletal protein can be performed in a number of
ways. For the purposes of this application comparing the content of
the cytoskeletal protein includes, but is not limited to, comparing
the content of the cytoskeletal protein of corresponding cell types
of two or more samples, comparing the correlation of cytoskeletal
protein content and cell size and/or cell granularity and/or other
cellular parameters, comparing the ratio of cytoskeletal protein
content of one cell type to the cytoskeletal protein content of
another cell type, and comparing the standard deviation, skewness,
kurtosis, or other features of the distribution of cytoskeletal
protein content.
[0037] The present invention provides, inter alia, methods of
profiling subjects based on the biophysical properties, including
the cytoskeleton signature, of their cells.
[0038] Unless noted otherwise, any method of assessing the content
of cytoskeletal protein in a cell, including, for example,
measuring the polymerization state of a cytoskeletal protein and/or
detecting fluorescence or other energy absorbed by, and/or emitted
from, the cytoskeletal protein or a label attached to the
cytoskeletal protein can be used in the present invention.
[0039] In some instances, it will be desirable to assess
cytoskeletal content and/or other cellular parameters associated
with live cells in order to assess live cytoskeletal protein
contents or to monitor live cell responses to stimuli. In other
instances, it will be desirable to assess cytoskeletal content in
cells that have been stabilized, i.e., by fixation. The present
invention includes both methods of assessing cytoskeletal protein
in live and stabilized cell samples. Methods of measuring actin
polymerization include, for example, fluorescence enhancement of
pyrene conjugates, DNase inhibition assays, viscosity measurements,
and spin-down assays. (Cooper et al., Methods in Enzymology, 1982,
182-211). For example, a plurality of cells obtained from a subject
can be mixed with an amount of pyrene conjugated actin and
polymerization can be measured with a fluorescence
spectrophotometer. Fluorescence is enhanced with polymerization. In
some embodiments, propidium iodide, which binds preferentially to
double-stranded DNA, can be used to correlate cell cycle
distribution with cytoskeletal protein response in each cellular
subset. Due to its ability to intercalate into double-stranded DNA,
propidium iodide can be used in ploidy analysis, hence cell cycle
analysis (Krishan, J. Cell. Biol. 1975, 66:188-193). Similarly,
propidium iodide or other agents can also be used to study
apoptotic cell death and to correlate apoptosis with cytoskeleton
changes as reflected in cytoskeletal protein measurements.
[0040] Cell cycles and apoptosis are non-limiting examples of
cellular responses that can be correlated with cytoskeletal protein
measurements according to methods of the present invention. One
skilled in the art would appreciate that other cellular responses
may also be studies and correlated with cytoskeletal measurements
according to methods disclosed herein.
[0041] It has been discovered by the present inventor that in order
to minimize the introduction of artifacts while assessing the
content of cytoskeletal protein and thus cellular responses, it is
preferable to minimize the handling of the cells and to mimic the
in vivo cellular environment. Accordingly, the present invention
provides methods of assessing the content of cytoskeletal protein
that do not involve the purification of cellular subsets. By
measuring the cytoskeletal protein in a sample that comprises a
mixture of cells, it is also possible to measure cytoskeletal
protein in a plurality of cell types simultaneously thereby
providing information about cytoskeletal protein content in a
plurality of cell types.
[0042] It has also been discovered by the present inventor that
cytoskeletal protein is not always adequately preserved when
stabilized at standard temperatures used for cell fixation. For
example, it has been discovered that at a temperature of about 4
degrees Celsius, a common temperature for cell fixation, accurate
measurement of the in vivo state of cellular actin contents is
impaired. It had been heretofore unknown that cytoskeletal
concentrations are extremely sensitive to temperature change.
Accordingly, in some embodiments, the present invention provides
methods of stabilizing mixtures of cells under conditions which
better preserve cellular cytoskeletal protein. In some embodiments,
conditions which better preserve cellular cytoskeletal protein are
temperature levels that are in the vicinity of physiological
temperature, for example, temperatures greater than about 25
degrees Celsius, preferably temperatures of about 30 degrees
Celsius, more preferably temperatures of about 35 degrees or 37
degrees Celsius or higher. In one embodiment, the cells are
stabilized at a temperature of from about 27 degrees to about 50
degrees Celsius. In another embodiment, the cells are stabilized at
a temperature of from about 30 degrees to about 40 degrees Celsius,
preferably at a temperature of about 37 degrees Celsius. According
to some embodiments, selected reagents and solutions used in the
present methods are pre-equilibrated to a temperature that better
preserves cytoskeletal protein.
[0043] It has also been discovered that calcium ion concentration
has an effect on the inducibility of cytoskeletal polymerization in
cells. Accordingly the present invention provides methods of
collecting a mixture of cells from a subject wherein the cells are
collected using a non-chelating anticoagulant. It had heretofore
been unknown that chelating agents have a distorting effect on
cytoskeletal protein levels and interfere with the ability to
accurately assess cytoskeletal protein content in a cell.
Accordingly, in some embodiments, conditions which better preserve
cellular cytoskeletal protein are those in which calcium
concentrations of the cells have not been altered. In some
embodiments, biological samples are collected in tubes containing
one or more non-chelating anticoagulants, e.g., heparins or
heparinoids. In some other embodiments, the tubes are maintained at
or near the selected temperature.
[0044] In addition to the artifacts arising from non-physiological
temperatures and altered calcium concentrations, other factors in
purification processes can affect the accuracy of the cytoskeletal
protein measurements, e.g., centrifugal forces. Research over the
past few years has shown that cellular behaviors can be
dramatically altered under different gravitational loadings. For
example, when cells are exposed to lower gravitational loading
(e.g., microgravity culture; Hashemi, FASEB J. 1999, 13: 2071-2082)
or hyper gravity (e.g., centrifugation), their responses to
stimulating agents are altered. Therefore, purification of specific
cell types by centrifugation can have a significant impact on
cellular skeletal protein contents or their polymerization state,
which in turn affect cellular responses to stimuli. Accordingly,
the present invention provides methods of minimizing the
introduction of artifacts in assessing the responsiveness of cells
by minimizing the manipulation following sample collection from
donors. For example, in some methods of the present invention, the
exposure of cells to high centrifugal forces prior to stabilization
of cells is avoided, e.g., forces over 200 g. In some methods of
the present invention, the exposure of cells to any centrifugal
forces prior to stabilization of cells is avoided.
[0045] Methods according to embodiments of the invention can be
used to simultaneously measure cytoskeletal protein contents in a
plurality of cell types in a mixture of cells. In some embodiments,
these methods are performed in a temperature range close to the
normal physiological temperatures, e.g., about 30.degree.
C.-40.degree. C., preferably around about 37.degree. C., to avoid
artifacts. Simultaneous measurements as used herein refer to
measurements of cytoskeletal protein contents in several cell types
in a mixture of cells without having to purify each cell type. As
used herein the term "simultaneousness" does not mean
chronologically at the same time. A plurality of cell types refers
to at least two or more cell types.
[0046] In some embodiments of the present invention, before
assessing the content of cytoskeletal protein associated with one
or more cells, the cells are stabilized. Methods of stabilizing
cells are known in the art and are thus not described herein in
detail. Cells can be stabilized, for example, by cross-linking
cellular protein, e.g., by fixation. Various chemicals including,
for example, alcohol, formaldehyde, or glutaraldehyde, can be used
to fix the cells. In some embodiments, the fixative solution will
contain additional ingredients. For example, the fixative solution
can also contain a membrane permeabilization agent, such as saponin
or other surfactants/detergents. An exemplary fixative solution
comprises about 3.7% formaldehyde and about 0.1% saponin in
PBS.
[0047] The present invention provides methods for measuring
cellular responses comprising a step of stabilizing a mixture of
cells comprising one cell type or a plurality of cell types from a
subject. Methods of collecting biological samples such as blood or
other tissues comprising one cell type or a plurality of cell types
are known and are thus not described herein in detail. In an
exemplary embodiment of the present invention, an aliquot of blood
sample is placed into each of a set of assay tubes. In some
embodiments, the blood samples and the assay tubes have been
pre-equilibrated to physiological temperature, e.g., about
37.degree. C. The cells are then stabilized by providing a selected
amount of a fixative solution to each assay tube. A fixative
solution is any solution that fixes the cells. Typically, a
fixative solution is a buffer solution (e.g., phosphate-buffered
saline, PBS) comprising one or more cell fixation reagents (e.g.,
formaldehyde or glutaraldehyde). The assay tubes are then incubated
at a selected temperature, e.g., from about 4 to about 50 degrees
Celsius, preferably from about 30 to about 40 degrees Celsius, for
a sufficient period of time in order to achieve stabilization of
the cells. Stabilization and permeabilization can be achieved in
multiple steps or in a single step. Any permeabilization solution
can be used in the present methods. For example, in some
embodiments, the permeabilization solution can comprises a
surfactant (e.g., saponin or other surfactants/detergents--triton,
alkyl glucosides, and the like.) in a buffer (e.g., PBS or other
buffers). The solution can further comprise of, for example,
additional ingredients such as a preservative or oxidation
inhibitor (e.g., sodium azide). A permeabilization solution can
comprise, for example, 0.1% saponin and 0.01% sodium azide in
PBS.
[0048] In some embodiments of the present invention, during
fixation of the sample, the sample is diluted in the fixative in
order to improve efficiency of the fixation as well as to reduce
fixation artifacts. For example, in some embodiments, the dilution
ratio will be from about 1:1 to about 50:1 (e.g., 1:1, 2:1, 5:1,
10:1 or 20:1), preferably from about 10:1 to about 30:1.
[0049] The methods of the present invention are not limited to
methods that include a step of assessing the content of
cytoskeletal protein. The described methods of stabilizing a
mixture of cells at temperatures of from about 27 degrees Celsius
to about 50 degrees Celsius, preferably at temperatures of from
about 30 degrees Celsius to about 40 degrees Celsius and more
preferably at about 37 degrees Celsius can be performed on any cell
sample. Accordingly, the present invention provides methods for
preserving a cell comprising stabilizing a mixture of cells
comprising one cell type or a plurality of cell types at
temperatures of from about 27 degrees Celsius to about 50 degrees
Celsius, preferably temperatures from about 30 degrees Celsius to
about 40 degrees Celsius and more preferably temperatures of about
37 degrees Celsius. In some embodiments, the mixture of cells will
be collected from a subject using a non-chelating anticoagulant.
Additionally, in some embodiments, exposure of the cells to high
centrifugal forces or even any centrifugation before stabilization
will be avoided.
[0050] In some embodiments of the present invention, blood is
collected at a remote site, stabilized at the remote site, and
transferred to an appropriate facility for further analysis. In
other embodiments, the blood is transferred before stabilization.
In some embodiments, the mixture of cells will be cells from tissue
culture and not from a particular subject.
[0051] After stabilization, the samples can, for example, be
centrifuged at a selected centrifugal force for a suitable period
of time to sediment the cells. The supernatants can be removed by
any known method, for example by decanting, siphoning, suction, or
filtration. In some embodiments a wash step is used to remove any
excess fixative. In some embodiments, staining solution can then be
added to each assay tube containing the sedimented cells in order
to label cell types and/or cytoskeletal protein.
[0052] In some embodiments of the present invention, the cellular
subsets or plurality of cell types are labeled with a cell-type
specific reagent. A cell-type specific reagent as used herein
refers to any reagent that can bind to and differentiate between
specific cell types. In some embodiments, a cell type-specific
reagent will comprise a reporter moiety, i.e., a detectable label,
to facilitate its detection. Reporter molecules are known in the
art and include, for example, fluorophores, chromophores,
radiolabels, such as radioisotopes, and affinity ligands, such as,
for example, biotin, glutathione, or an oligonucleotide that can be
specifically detected by addition of a labeled reagent such as, for
example, avidin/strepavidin, glutathione S-transferase, or a
complementary oligonucleotide. An oligonucleotide affinity ligand
can be a synthetic oligonucleotide or a naturally occurring
oligonucleotide. It can be, for example, DNA (deoxyribonucleic
acid), RNA (ribonucleic acid), or the like (e.g., peptide nucleic
acid, PNA). An oligonucleotide should have a sufficient length such
that the binding to its complementary oligonucleotide will be
stable at the temperature used for the experiments; typically,
10-mers or longer. The particular reporter molecule or detectable
group used is not a critical aspect of the invention. It can be any
material having a detectable physical or chemical property. Thus, a
reporter molecule or label is any composition detectable by, for
example, spectroscopic, photochemical, biochemical, immunochemical,
electrical, optical or chemical means. Additional examples include,
magnetic beads, fluorescent dyes, enzymes, and colorimetric labels
such as colloidal gold or colored glass or plastic beads. For
example, useful labels include .sup.32P, fluorescent dyes,
electron-dense reagents, enzymes (e.g., as commonly used in an
ELISA), biotin, digoxigenin, or haptens and proteins which can be
made detectable, e.g., by incorporating a radiolabel into the
cytoskeletal protein or used to detect antibodies specifically
reactive with the cytoskeletal protein.
[0053] In some embodiments of the present invention, a cell
type-specific reagent can also comprise a binding agent that binds
to cell-specific membrane proteins. For example, CD56 molecules are
typically found on neural cells, tumors, and lymphocytes that
mediate non-MHC-restricted cytotoxicity. Thus, a binding agent,
such as for example, an antibody, that binds specifically or
preferentially to CD56 can be used to specifically label this
subpopulation of lymphocytes. Similarly, CD3 molecules are
typically found on mature T lymphocytes (T cells) and these
molecules associate with T-Cell receptors (TCR); hence, antibodies
against CD3 can be used to label this population of T cells. CD14
is a glycolipid-anchored membrane glycoproteins expressed on cells
of the myelomonocyte lineage, including monocytes, macrophages, and
some granulocytes. Thus, antibodies against CD14 can be used to
label these types of cells. A skilled practitioner will be able to
choose a suitable binding agent for use in the present
invention.
[0054] In some embodiments of the present invention, the reporter
moiety will be a fluorescent molecule and the labeled cells will be
detected using microscopy techniques or flow cytometry techniques,
e.g., in some embodiments a fluorescence-activated cell sorter
(FACS) will be used. Examples of fluorescent cell-specific labeling
reagents include, for example, those sold by Beckon Dickinson and
Company (Franklin Lakes, N.J.) under the trade names of
perCP-CD3.TM. and APC-CD14.TM..
[0055] Cytoskeletal protein analysis does not require labeling of
cell types. For example, the total cytoskeletal protein content in
the mixture of cells can be assessed and provide information as to
cellular responses, cytoskeletal signatures, biomolecular
signatures, and the like.
[0056] In some embodiments, cytoskeletal protein is labeled.
Cytoskeletal protein labeling solution as used herein refers to
solution containing one or more reagents that can bind specifically
to a certain type of skeletal protein as opposed to other
molecules. For example, actin-labeling solution as used herein
refers to a solution containing one or more reagents that can bind
specifically or preferentially to actin molecules (G-actin or
F-actin, or both), as opposed to other molecules.
[0057] Any reagent, including probes, that can preferentially bind
to cytoskeletal protein such as, but not limited to, actin
microfilaments, intermediate filaments, microtubules, spectrin,
talin, vinculin, desmin, senaptin, vimentin, ezrin, moesin,
filamin, phakinin, actinin, profilin, fibrin, keratin, myosin,
dynein, and kinesin can be used in the present invention to assess
the cellular content of cytoskeletal protein. Reagents that
preferentially bind to actin molecules include, for example,
anti-actin antibody, cytochalasin D, phalloidin, and phallacidin.
Cytochalasin D binds to the plus ends of F-actin filaments and
prevents further addition of G-actin. Phalloidin and phallacidin
are cyclic peptides from the Death Cap fungus (Amanita pkalloides)
that bind to and stabilize F-actin filaments. Reagents that
preferentially bind to tubulin include, for example, anti-tubulin
antibodies, paclitaxel, paclitaxel conjugates, and BODIOYP FL
vinblastine. Reagents that preferentially bind to other
cytosekeleton proteins include, for example, phosphoinositides and
related products, anti-glial fibrillary acid protein antibody,
anti-desmin antibody, anti-synapsin antibody, and endostatin
protein.
[0058] Cytoskeletal protein binding reagents are typically coupled
to a reporter moiety to facilitate their detection. A reporter
moiety can include, for example, a fluorophore, a chromophore, a
radio isotope, or an affinity ligand, such as, for example, biotin
or an oligonucleotide that can be specifically detected by the
addition of a labeled reagent, for example, avidin or the
complementary oligonucleotide. Commonly used fluorophores can
include, for example, NBD, 7-nitrobenz-2-oxa-1,3-diazol-- 4-yl;
FITC, fluorocein isothiocyanate; and BODIPYTM,
4,4,-difluoro-3a,4a-diaza-s-indacene. A reagent that contains an
actin-binding moiety and a reporter moiety will be referred to
herein as an "actin probe". Molecular Probes, Inc. (Eugene, Oreg.)
offers various labeled phalloidin and phallacidin, including those
under the trade names of BODIPY.TM.-phalloidins and
BODIPY.TM.-phallacidins with different excitation and emission
wavelengths. An exemplary F-actin labeling solution, e.g., F-actin
probe, can be prepared by drying 30 ul of a methanol stock solution
of BODIPY.TM.-phallacidin, which has been prepared according to the
instructions from the supplier, in a glass tube, followed by
addition of the permeabilization solution as described above. The
particular reporter molecule or detectable group used is not a
critical aspect of the invention. It can be any material having a
detectable physical or chemical property. Thus, a reporter molecule
or label is any composition detectable by spectroscopic,
photochemical, biochemical, immunochemical, electrical, optical or
chemical means. Additional examples include, magnetic beads,
fluorescent dyes, enzymes, and colorimetric labels such as
colloidal gold or colored glass or plastic beads.
[0059] Cytoskeletal protein assessment does not require labeling of
cytoskeletal protein in the cells. For example, cytoskeletal
protein content can be determined by measuring the absorption
profiles of the cells at various wavelengths of light.
[0060] In some embodiments, the cells can be suspended in the
staining, i.e., labeling, solution by tapping, mixing, shaking, or
the like, followed by a period of incubation time for labeling of
the cytoskeletal protein to occur. A selected amount of a wash
solution, for example, PBS containing 0.1% saponin and 0.01% sodium
azide, can be added to each assay tube followed by centrifugation
to sediment the cells. The supernatants are discarded, for example
by decantation, and the cells re-suspended in a suitable amount
storage solution.
[0061] Some embodiments of the present invention involve the step
of assessing the content of cytoskeletal protein associated with a
cell. Any method of assessing the content of cytoskeletal protein
can be used in the present invention. The step of assessing the
content of cytoskeletal protein can be performed at a remote
location. In embodiments of the present invention wherein the
cytoskeletal protein is labeled, assessing the content of the
cytoskeletal protein can be as simple as detecting the label. Means
of detecting labels are well known in the art. Thus, for example,
where the label is a radioactive label, means for detecting can
include a scintillation counter or photographic film as in
autoradiography. Where the label is a fluorescent label, it can be
detected by exciting the fluorochrome with the appropriate
wavelength of light and detecting the resultant fluorescence. The
fluorescence may be detected visually, by the use of electronic
detectors such as charge coupled devices (CCDs) or photomultipliers
and the like. Similarly, enzymatic labels may be detected by
providing the appropriate substrates for the enzyme and detecting
the resultant reaction product. Colorimetric or chemiluminescent
labels can be detected by simply observing the color associated
with the label. Similarly, embodiments of the invention can be
adapted to miniature assay formats (e.g., 96-wells plates, chips,
and the like.). Furthermore, various steps as described above may
be performed automatically by machines.
[0062] Methods known to those of skill in the art for detection of
nucleic acids and proteins can be used, for example, PCR, northern
and Southern blots, dot blots, nucleic acid arrays, western blots,
immunoassays such as immunoprecipitation, ELISA, proteomics assays,
and the like.
[0063] In some embodiments, the cell content is assessed by flow
cytometry for the measurements of intracellular levels of
cytoskeletal protein in cellular subsets. There are several
commercially available flow cytometers, including FACS instruments,
that can be used in these methods; they are not part of the
invention and should not limit the present invention. One exemplary
flow cytometer is sold by Becton Dickinson and Company (Franklin
Lakes, N.J.) under the trade name of FACSCalibur.TM..
[0064] Additional biophysical cellular parameters can be assessed
in addition to the content of cellular cytoskeletal protein. For
example, an index of cell size and cell granularity can be assessed
using the methods of the present invention. In some embodiments of
the present invention, forward and side scatter data can be used as
a proxy for cell size and granularity respectively. For example,
when a laser hits the cell, the larger the cell the more photons of
light it scatters. By measuring the light scattered on the side of
a cell furthest from where the laser hits the cell, a measure of
cell size can be obtained. Similarly, the more granular a cell the
more light it will scatter 90 degrees to the incident laser beam
(i.e. side scatter). A cell that has more dense granules will
scatter more light to the side. Additional parameters, include, but
are not limited to, cellular absorption, autofluorescence, cell
count ratios (e.g., looking at CD4/CD8 cell ratios), and receptor
count ratios on the cell surface.
[0065] As previously described, flow cytometry can be used to
assess the cytoskeletal protein of the cell as well as to assess
cell size and granularity. Methods of using flow cytometry to
measure cellular biophysical parameters are known in the art and
are thus not described herein in detail. In one embodiment, flow
cytometry gating techniques are used to assess the biophysical
cellular parameters. Flow cytometry can be used to gate on a
plurality of cell types and obtain measurements from the different
cellular subtypes. For example, in one embodiment of the present
invention, flow cytometry is used to simultaneously assess the
F-actin content of T-cells, monocytes and neutrophils. Three gates
are defined to select for different cells. For example, neutrophils
are selected based on the Side Scatter/Forward Scatter histogram;
T-cells are selected based on the CD3 channel; and monocytes are
selected based on the CD14 channel. The actin content of each cell
type is then measured using the actin channel and the fluorescence
level of each cell can be displayed on a corresponding histogram of
each cell type. In this manner, the relative mean fluorescence
associated with the F-actin content of each cell population is
calculated using statistical analysis of the data. Other cellular
sub-populations can readily be analyzed with this technique. For
example, probes for CD4 and CD8 may be used to further
differentiate the helper and cytotoxic T cells subgroups,
respectively. Other cytoskeletal proteins can also be readily
analyzed with this technique.
[0066] To improve measurement reliability, each cellular sample can
be analyzed, for example, by flow cytometry in duplicate or
triplicate. The average cytoskeletal protein fluorescence and the
standard error of mean can then be calculated for each data point.
For example, in one embodiment, an experiment with 6 donors over 4
time points during a 24 day period will consist of 4 blood
collections per donor. In one example, 90 ul of blood from each
blood sample is cultured in each of six assay tubes at 37.degree.
C. Three of the assay tubes receive a stimulation (for example,
OKT3 to activate the T-cells) and the other three are used as
control. That is, the experiment is performed in triplicate. After
sample processing, each tube is analyzed by flow cytometry to
generate one data point for each cellular subpopulation.
[0067] Using methods of the present invention, convenient
measurements of cytoskeletal protein in or on the surface of
various cells can be performed in a plurality of cells without
having to first purify each cell. These methods make it possible to
study cytoskeletal protein/differences in various cells as a
function of time or among individuals. For example, it is possible
to follow the cellular cytoskeletal protein contents and to monitor
the inducible cellular cytoskeletal protein contents of each cell
type for each test subject over time.
[0068] The present invention provides methods of identifying the
cytoskeletal signature of cells, biomolecular signature of cells as
well as cellular responses to agents that have an effect when
provided to a cell, e.g., biologically active agent. Such agents,
for example, can act as either stimulants or depressants. For use
herein, a stimulant is any biologically active agent that produces
a temporary or permanent increase in the functional activity or
efficiency of an organism or any of its parts. For use herein, a
depressant is any biologically active agent that produces a
temporary or permanent decrease in the functional activity or
efficiency of an organism or any of its parts. In some embodiments,
the biologically active agent will be neither a stimulant nor a
depressant but will have a measurable effect on the cytoskeletal or
biomolecular signature of a cell.
[0069] In certain embodiments, the biologically active agent will
be exogenously administered to the mixture of cells or plurality of
cell types after the cells have been obtained from the subject. In
some other embodiments, the subject will have been exposed to the
agent or suspected to have been exposed to the agent before
collection of the cells. In some embodiments, the mixture of cells
will be cells from tissue culture and not from a particular
subject. Any biologically active agent can be used in the present
methods in order to, for example, measure the cellular effect of
the agent or identify a cytoskeletal or biomolecular signature
associated with the agent. Such agents include, but are not limited
to, pathogens (i.e., bacterial or viral toxins), and small
molecules (i.e., drugs, peptides, and the like). These include, for
example, antigens, antibodies, superantigens, chemotatic agents,
chemotatic peptides, enzyme regulators, immune modulators,
chemotherapeutic agents, FMLP,
N-formyl-methionyl-leucyl-phenylalanine; protein kinase C
activator, phorbol myristate acetate, PMA; anti-TCR/CD3 mAb;
lectins; lipopolysacharides. LPS; calcium ionophores, A23187 or the
like; ligands, and the like.
[0070] Pathogens include, but are not limited to, bacteria
including gram-positive and gram-negative bacteria, fungi,
parasites, viruses, and other chemical and biological toxins.
[0071] Bacteria include, but are not limited to, bacteria from the
following species Aerococcus, Enterococcus, Halococcus,
Leuconostoc, Micrococcus, Mobiluncus, Moraxella catarrhalis,
Neisseria (including N. gonorrheae and N. meningitidis),
Pediococcus, Peptostreptococcus, Staphylococcus (including S.
aureus, S. epidermidis, S. faecalis, and S. saprophyticus),
Streptococcus (including S. pyogenes, S. agalactiae, S. bovis, S.
pneumoniae, S. mutans, S. sanguis, S. equi, S. equinus, S.
thermophilus, S. morbillorum, S. hansenii, S. pleomorphus, and S.
parvulus), Veillonella; Acetobacter, Acinetobacter, Actinobacillus
equuli, Aeromonas, Agrobacterium, Alcaligenes, Aquaspirillum,
Arcanobacterium haemolyticum, Bacillus (including B. cereus and B.
anthracis), Bacteroides (including B. fragilis), Bartonella,
Bordetella (including B. pertussis), Brochothrix, Brucella,
Burkholderia cepacia, Calymmatobacterium granulomatis,
Campylobacter (including C. jejuni), Capnocytophaga, Caulobacter,
Chromobacterium violaceum, Citrobacter, Clostridium species
(including C. perfringens, C. tetani and C. difficile), Comamonas,
Curtobacterium, Edwardsiella, Eikenella, Enterobacter, Erwinia,
Erysipelothrix, Escherichia species (including E. coli),
Flavobacterium (including F. meninosepticum), Francisella species
(including F. tularensis), Fusobacterium (including F. nucleatum),
Gardnerella (including G. vaginalis), Gluconobacter, Haemophilus
(including H. influenzae and H. ducreyi), Hafnia, Helicobacter
(including H. pylori), Herpetosiphon, Klebsiella species (including
K. pneumoniae), Kluyvera, Lactobacillus, Legionella species
(including L. pneumophila), Leptotrichia, Listeria species
(including L. monocytogenes), Microbacterium, Morganella,
Nitrobacter, Nitrosomonas, Pasteurella species (including P.
multocida), Pectinatus, Porphyromonas gingivalis, Proteus species
(including P. mirabilis), Providencia, Pseudomonas (including P.
aeruginosa, P. mallei, P. pseudomallei and P. solanacearum),
Rahnella, Renibacterium salmoninarum, Salmonella, Serratia,
Shigella, Spirillum, Streptobacillus species (including S.
moniliformis), Vibrio (including V. cholerae and V. vulnificus),
Wolinella, Xanthobacter, Xenorhabdus, Yersinia species (including
Y. pestis and Y. enterocolitica), Zanthomonas, Zymomonas,
Crenothrix, Leptothrix Sphaerotilus, Beggiatoa, Gallionella,
Sulfolobus, Thermothrix, Thiobacillus species (including T.
ferroxidans), Thiomicrospira and Thiosphaera, Desulfobacter,
Desulfobulbus, Desulfococcus, Desulfomonas, Desulfosarcina,
Desulfotomaculum, Desulfovibrio, Desulfuromonas, Treponema species
(including T. pallidum, T. pertenue, T. hyodysenteriae and T.
denticola), Borrelia species (including B. burgdorferi and B.
recurrentis), Leptospira and Serpulin, Acetobacterium, Actinomyces
species (including A. israelii), Bifidobacterium, Brevibacterium,
Corynebacterium species (including C. diphtheriae, C. insidiosum,
C. michiganese, C. rathayi, C. sepedonicum, C. nebraskense),
Dermatophilus, Eubacterium, Mycobacterium species (including M.
tuberculosis and M. leprae), Nocardia, Propionibacterium,
Rhodococcus, Streptomyces, Chondromyces, Cystobacter, Melittangium,
Myxococcus, Nannocystis, Polyangium and Stigmatella, Mycoplasma
species (including M. pneumoniae), Spiroplasma and Ureaplasma
species (including U. urealyticum), Aegyptianella, Anaplasma,
Chlamydia species (including C. pneumoniae, C. trachomatis and C.
psittaci), Cowdria, Coxiella, Ehrlichia, Eperythrozoon,
Haemobartonella, Neorickettsia, Rickettsia and Rickettsiella.
[0072] Fungi include but are not limited to, Acremonium,
Aspergillus species (including A. flavus, A. niger, A. fumigatus,
A. terreus, A. glaucus, and A. nidulans), Blastomyces species
(including B. dermatitidis), Candida species (including C. albicans
and C. parapsilosis), Ceratocystis, Chaetomium, Coccidioides
species (including C. immitis), Cryptococcus species (including C.
neoformans and C. laurenti) Epidermophyton, Fusarium species
(including F. oxysporum and F. solani), Gongronella, Histoplasma
species (including H. capsulatum), Acremonium, Honnonea,
Lasiodiplodia theobromae, Malassezia furfur, Microsporum,
Mycosphaerella fijiensis, Paracoccidiodes brasiliensis,
Penicillium, Pneumocystis carinii, Pseudallescheria boydii,
Pythium, Rhizoctonia, Rhodotorula, Saccharomyces, Sporothrix
schenckii, Torula, Trichoderma, Trichophyton species (including T.
mentagrophytes and T. rubrum) and Trichothecium.
[0073] Parasites include, but are not limited to, Acanthamoeba
species, Ascaris lumbricoides, Babesia, Balamuthia, Balantidium,
Blastocystis species including B. hominis, Chilomastix, Clonorchis
sinensis, Cryptosporidium parvum, Cyclospora, Dientamoeba fragilis,
Diphyllobothrium, Echinococcus, Endolimax, Entamoeba species
(including E. histolytica), Enterobius species (including E.
vermicularis), Giardia lamblia, hookworms (including Necator,
Ancylostoma, and Unicinaria), Hymenolepsis, lodamoeba, Isospora,
Leishmania, Mansonella, microsporidia, Microsporidium, Naegleria
fowleri, Onchocerca, Plasmodium (including P. falciparum, P. vivax,
P. ovale and P. malariae), Schistosoma (including S. haematobium
and S. mansoni), Strongyloides species (including S. stercoralis),
tapeworms (including Taenia species), Toxoplasma (including T.
gondii), Trichinella (including T. spiralis), Trichomonas
vaginalis, Trichuris species including T. trichiura, Trypanosoma,
Dirofilaria, Brugia, Wuchereria, Vorticella, Eimeria species,
Hexamita species and Histomonas meleagidis.
[0074] Chemical and biological toxins include any chemical or
biological toxin including chemical or biological warfare agents. A
chemical or biological warfare agent is any agent that might be
employed because of its direct toxic effect on humans, animals, and
plants. Accordingly, all chemical substances whether gaseous,
liquid or solid, which are developed, produced, stockpiled, and
used for hostile purposes and whose toxic effects are used to
interfere with or destroy the normal functions of humans, plants,
or animals in such a way as to lead to death, temporary
incapacitation, or permanent injury are encompassed by the term
chemical warfare agent. In some embodiments, the poisonous effects
may occur immediately. In others, poisonous effects may be delayed.
Chemical warfare agents may be delivered by any means known to
deliver harmful or non-harmful agents, for example, by artillery,
bombs, grenades, missiles, spraying devices, dumping devices,
postal system, and the like.
[0075] Exposure to a specific class of chemical warfare agents,
commonly referred to as organophosphorus agents, is recognizable
using the methods of the present invention. Organophosphorus agents
include the class of warfare agents known as nerve agents. Nerve
agents include any organophosphate ester derivative of phosphoric
acid that causes a disruption in the normal neurological function
of a human, animal, or plant. Examples include VE
(O-Ethyl-S-[2-(diethylamino)ethyl] ethylphosphonothioate), VG
(O,O-Diethyl-S-[2-(diethylamino)ethyl] phosphorothioate), VM
(O-Ethyl-S-[2-(diethylamino)ethyl]methylphosphonoth- ioate), VX
(O-Ethyl-S-[2(diisopropylamino)ethyl]methylphosphonothioate),
cyclosarin, sarin, tabun, and soman.
[0076] The present invention provides methods of providing a
biologically active agent (e.g., stimulant or depressant) to a
mixture of cells before cell stabilization in order to, for
example, measure the cellular effect of the agent or identify a
cytoskeletal or biomolecular signature associated with the agent.
In some embodiments, the mixture of cells is incubated in a
suitable buffer, for example, Hanks' buffer, with a cellular
stimulant or depressant. The reagent's stimulant or depressant
effects can then be calculated from changes in the cytoskeletal
signature or biomolecular signature of the cell. This method can be
used to study biological samples that are exposed to a number of
reagents specific for the various cell types.
[0077] In some embodiments, the present invention provides methods
of characterizing the impact of a pathogen (or other biologically
active agent) on the cytoskeletal signature of one cell type or a
plurality of cell types. The signature profile of cell types to
pathogens can be evaluated through measurements of cytoskeletal
signature in one cell type or a plurality of cell types as well as
cell size index and granularity index of the cells types before and
after exposure to a pathogen. The impact of each pathogen on the
cytoskeletal signature can be measured at increasing concentration
of pathogen (increasing toxin concentration or multiplicity of
infection) to obtain a dose response curve for each pathogen as
well as a time-course of evolution of the cytoskeletal protein
signature. Statistical analysis routines can be performed to
develop predictive models for identification of pathogen exposure
by analyzing the cytoskeletal or biomolecular signature of the cell
types. In this manner, controls can be created for comparison with
samples from a subject suspected of having a disease state. By
comparing the cytoskeletal signatures from a subject sample to the
cytoskeletal signature of a control, a skilled practitioner can
determine if a subject has been exposed to a particular pathogen.
For example, in one embodiment, a mixture of cells comprising one
cell type or a plurality of cells types will be exposed to a
pathogen, such as for example, salmonella typhimurium. The content
of cytoskeletal protein in the one cell type or plurality of cell
types before and after exposure to the pathogen will be assessed as
well as in some embodiments, other cellular parameters such as cell
granularity and cell size of the plurality of cell types. Based
upon the cytoskeletal protein content and in some embodiments, cell
size and granularity, the cellular cytoskeletal signature in
response to salmonella typhimurium infection will be determined and
will act as the control. In order to determine if a subject has
been exposed to salmonella typhimurium, the content of cytoskeletal
protein in a corresponding cell types from the subject will be
assessed as well as in some embodiments, the cell size and
granularity of the corresponding cell types. The cellular
cytoskeletal signature from the subject will then be compared to
that of the control to determine if the subject has been exposed to
salmonella typhimurium.
[0078] In this manner, it is possible, for example, to assess the
presence or absence of a disease state and other clinical
parameters in a subject. A clinical parameter is not limited to the
presence or absence of a disease state but can also include, for
example, risk of disease, state of disease, severity of disease,
class of disease, response to treatment of disease, i.e., whether a
subject will be a negative responder, positive responder, or
non-responder, and the like.
[0079] According to the present invention, a practitioner will be
able to use the assessment of cytoskeletal protein content
associated with a subject's cells to qualify the status of the
subject with respect to the clinical parameter. In some
embodiments, a certain cellular cytoskeletal signature and/or
biomolecular signature will be indicative of a clinical parameter
associated with a specific disease state. Accordingly, the present
invention provides methods of correlating specific measurements of
actin cytoskeleton in one or a plurality of cell types with
specific clinical parameters.
[0080] The cytoskeletal protein profile of any pathogen or other
biologically active agent can be determined using the present
methods.
[0081] Methods of the invention are useful in comparing
cytoskeletal signatures or biomolecular signatures of a large
number of subjects. For example, some methods of the present
invention include the step of assessing the content of cytoskeletal
protein in one cell type or a plurality of cell types from each of
a plurality of subjects belonging to a least two population groups
differing with respect to at least one clinical parameter
associated with a disease state and comparing the content of
corresponding cytoskeletal protein in said one cell type or
plurality of cell types from said groups to each other to create
cytoskeletal protein profiles that are associated with different
clinical parameters. In this manner, changes in the cytoskeletal
signature or biomolecular signature of the cells can be
determined.
[0082] Cytoskeletal contents in various cells from various samples
can be readily compared, for example, by plotting on the same graph
for each time point to determinate sample-to-sample variability.
With a large number of samples, it is possible to establish
"normal" distributions for each cellular subset as a baseline for
analysis of data from a specific study--the baseline cytoskeletal
signature or biomolecular signature. In other words, the "normal"
distributions of cytoskeletal contents in each cellular subset can
be used in other studies, for example, in patient diagnosis. In
addition, the "normal" cytoskeletal protein contents in various
cells can be used as cellular signatures for screening general
populations for disease outbreaks, effects of chemical or
biological warfare/terrorism, and the like.
[0083] Embodiments of the present invention which allow
simultaneous measurements of cytoskeletal protein associated with
blood cellular subsets provide unique tools for rapidly assessing
the status of cells and their responsiveness using the cytoskeletal
protein levels as the signature of each cellular subset. These
methods can be applied, for example, in clinical research, patient
diagnostics, and individualized therapy where an evaluation of the
status of blood cells and their responsiveness to various agents
are useful for diagnosis of disease and evaluation of treatment
protocols. It is also valuable, for example, in situations where
evaluation of responsiveness of cells is needed.
[0084] In one application, the present methods can be used for
individualized drug therapy in order to select the most appropriate
drug for treatment of a patient. Typically, when a patient presents
a condition to a physician, the physician has a multitude of drugs
that he can use for treatment of the condition. Typically, the
physician will randomly select one of the drugs for therapy. If the
drug is not a good fit, the patient will return to the physician
and receive a second prescription for another drug in the hopes
that the second drug will be a better fit than the first. Using the
methods of the present invention, a blood sample can be taken from
the patient at the first visit and the responsiveness, of the
subject to a select group of drugs can be determined by identifying
the cytoskeletal signature of the circulating blood cells in
response to the different drugs. A certain signature will be
indicative of a positive responder, negative responder, or a
non-responder. In this manner, the efficacy of the drug, the
optimal dosage concentrations, and/or the potential side effects of
the drug can be assessed before the drug is provided to patient.
The appropriate drug can then be provided in the first instance
thereby leading to improved patient outcomes and reducing the
overall cost of treatment. In some embodiments, additional cellular
parameters, such as, for example, cell size and shape, will be
identified to provide a biomolecular signature that is indicative
of a positive responder, negative responder, or a
non-responder.
[0085] Methods of the present invention can be used to reduce the
risk associated with drug development by identifying unique
signatures associated with adverse side effects thereby enabling
market introduction of otherwise failing drugs and reducing the
overall cost of drug development. For example, during phase III
clinical trial, if a drug is effective in 70% of patients but
causes unacceptable side effects in the remaining 30%, it is highly
unlikely that the drug will come to the market. Using the present
methods, it is possible to identify the patients that will be
positive responders, negative responders, or non-responders by
their cellular signatures. Before prescribing the drug to a patient
population, the patients will be screened to determine how they
will respond to the drug, i.e. a blood or tissue sample will be
obtained from the patient and the cytoskeletal profile of the cells
will be assessed. Those that have cytoskeletal profiles that match
the cytoskeletal profiles of patients that didn't respond well to
the drug will be advised to take an alternative drug.
Alternatively, patients that have cytoskeletal protein profiles
indicating that they will be positive responders will be prescribed
the drug. In some embodiments, additional cellular parameters, such
as, for example, cell size and shape, will be identified and the
patient's biomolecular signatures will be identified and
subsequently matched.
[0086] The methods of the present invention can be used in drug
discovery. The effect of a select compound on cellular signatures
can be assessed and it can thereby be determined whether the drug
will be effective in treating a condition or disease state.
[0087] The present methods are particularly useful for diagnosing
conditions, evaluating whether certain drugs will have a desired
effect, and determining prognoses. Immune-related diseases such as,
for example, allergies; autoimmune diseases such as, for example,
arthritis and lupus; immune related syndromes such as, for example,
Wiskott Aldrich; cancers such as for example, leukemia; and
multiple sclerosis are only a small subset of the diseases
detectable by the present methods. Other exemplary diseases
include, but are not limited to, for example, stroke, nephritis,
renal fibrosis, chronic obstructive pulmonary disease, restenosis,
renovascular disease, organ transplant rejection; diseases
associated with abnormal angiogenesis; insulin resistance; vascular
inflammation; cerebrovascular diseases; hypertension; respiratory
diseases, such as asthma; heart failure; arrhythmia; angina;
atherosclerosis; kidney failure; peripheral vascular disease;
peripheral arterial disease; acute vascular syndromes;
microvascular diseases; hypertension; Type I and II diabetes and
related diseases; hyperglycemia; hyperinsulinemia; coronary heart
disease; bacterial disease and viral disease, such as AIDS. By
assessing the evolution of the cytoskeletal protein signature or
biomolecular signature at different times during disease
progression, the stage of disease can be determined as well as the
likely prognosis.
[0088] The present methods can be used to create an inflammation
index that categorizes inflammation at different stages by
measuring cytoskeletal protein levels or biomolecular signatures at
different stages of inflammation. The present methods can also be
used to detect hormonal changes in the body by associating certain
hormonal changes to cytoskeletal protein signatures and/or
biomolecular signature. In this manner, early assessment of
diseases can be made by detecting internal changes that precede a
disease.
[0089] Donor-recipient compatibility for transplants can be
assessed using the present methods. Cytoskeletal protein
measurements can be taken pre-transplant to assess compatibility
and reduce the risk of rejection. For example, a tissue sample can
be obtained from the donor and a blood sample from the recipient.
By determining the cytoskeletal or biomolecular signature of the
recipient's cells, e.g., T cells, after contact with the tissue
sample, it can be determined how the recipient will respond to the
transplant. Cytoskeletal protein measurements can be taken
post-transplant to detect early rejection or acceptance. The
present methods can also be used to optimize immunosuppressant
therapy by monitoring the cytoskeletal protein signatures or
biomolecular signatures of immune cells.
[0090] The extent of radiation exposure can be assessed using the
present methods. Certain cytoskeletal protein signatures or
biomolecular signatures will be indicative of different levels of
radiation exposure and cellular damage.
[0091] The present methods can be used for early detection of
cancer as well as for the optimization of treatment protocols and
analysis of biopsy samples. The present methods can also be used to
optimize chemotherapy through assessment of the cytoskeletal or
biomolecular signature of the patient.
[0092] The present invention also provides methods of screening
blood samples, i.e., for blood blanks, in order to identify blood
that is not fit for donation. Vaccine development and validation is
included within the scope of the present invention. The present
methods can be used to identify adverse reactions to vaccines and
screen for adverse reactions prior to vaccination. For example, a
child can be screened for a potential adverse reaction to a panel
of vaccines prior to vaccination. If a negative reaction is
detected, a second screen can be performed to determine which of
the vaccines should not be administered. In addition, tests can be
developed to determine the effectiveness of a vaccine in an
individual, i.e., to determine whether the vaccine generated an
immune readiness. The longevity of a vaccine in an individual can
also be assessed.
[0093] The present methods can be used to screen the food supply
for disease, e.g., evidence of bacterial infection or mad cow
disease as well as for animal heath, e.g., rabies. The present
methods can be used to assess aging in a subject by correlating
cytoskeletal protein signatures or biomolecular signatures in
certain cell types with aging.
[0094] The present invention provides methods for classifying cells
and generating classification systems for classifying cells. In
accordance with some embodiments, the method comprises a step of
providing a learning set comprising a plurality of data objects.
Each data object represents a biomolecular signature for which
clinical data has been developed. The clinical data included in the
data object includes biophysical cellular parameters such as, for
example, content of cytoskeletal protein, cell size, cell shape and
the like. Each cell sample is classified into one of at least two
different clinical parameter classes. For example, the clinical
parameters could include presence or absence of disease, risk of
disease, stage of disease, response to treatment of disease or
class of disease.
[0095] In some embodiments, the method can further comprise a step
of training a classification algorithm with the learning set.
Classification models can be formed using any suitable statistical
classification (or "learning") method that attempts to segregate
bodies of data into classes based on objective parameters present
in the data. Classification methods can be either supervised or
unsupervised. Examples of supervised and unsupervised
classification processes are described in Jain, "Statistical
Pattern Recognition: A Review", IEEE Transactions on Pattern
Analysis and Machine Intelligence, Vol. 22, No. 1, January
2000.
[0096] In supervised classification, each data object includes data
indicating the clinical parameter class to which the sample
belongs. Examples of supervised classification processes include
linear regression processes (e.g., multiple linear regression
(MLR), partial least squares (PLS) regression and principal
components regression (PCR)), binary decision trees (e.g.,
recursive partitioning processes such as CART--classification and
regression trees), artificial neural networks such as back
propagation networks, discriminant analyses (e.g., Bayesian
classifier or Fischer analysis), logistic classifiers, and support
vector classifiers (support vector machines).
[0097] In other embodiments, the classification models that are
created can be formed using unsupervised learning methods.
Unsupervised classification attempts to learn classifications based
on similarities in the training data set. In this case, the data
representing the class to which the sample belongs is not included
in the data object representing that subject, or such data is not
used in the analysis. Unsupervised learning methods include cluster
analyses. Clustering techniques include the MacQueen's K-means
algorithm and the Kohonen's Self-Organizing Map algorithm.
[0098] Learning algorithms asserted for use in classifying
biological information are described, for example, in PCT
International Publication No. WO 01/31580 (Barnhill et al.,
"Methods and devices for identifying patterns in biological systems
and methods of use thereof"), U.S. Patent Application 2003 0004402
A1 (Hitt et al., "Process for discriminating between biological
states based on hidden patterns from biological data"), and U.S.
Patent Application 2003 0055615 A1 (Zhang and Zhang, "Systems and
methods for processing biological expression data").
[0099] Thus classification model can be generated that classify a
sample into one of the classification groups. The classification
models can be used to classify an unknown sample into one of the
groups.
[0100] The present invention also provides methods for maintaining
a cytoskeletal protein signature or biomolecular signature registry
system or database. Such a system can be managed using
bioinformatics. Bioinformatics is the study and application of
computer and statistical techniques to the management of biological
information. Thus, in one embodiment, the present invention
provides a method for populating a database for further medical
characterization. For example, a database can be populated with the
cytoskeletal protein signatures in a plurality of cell types that
have been exposed to agents that act as, for example, stimulants,
depressants, pathogens, bacterial and viral toxins, chemical
warfare agents and the like. This information can be used for
comparative purposes as a control. Once a database of sufficient
size has been generated, clinical parameters can be determined by
comparing cytoskeletal protein measurements and other cellular
parameters in a plurality of cell types to corresponding
cytoskeletal protein measurements and other cellular parameters in
the controls.
[0101] In another embodiment, the present invention also provides
an apparatus for automating the methods of the present invention,
the apparatus comprising a computer and a software system capable
of analyzing biomolecular signatures. The data is inputted in
computer-readable form and stored in computer-retrievable format.
The present invention also provides computer-readable medium
encoded with a data set comprising cellular profiles of cells that
have been exposed to agents that act as stimulants or depressants,
pathogens, bacterial and viral toxins, chemical warfare agents and
the like. The information in the data set can be used for
comparison purposes.
[0102] The methods described herein for quantifying cellular
cytoskeletal protein and other cellular parameters provides
information which can be correlated with pathological conditions,
predisposition to disease, therapeutic monitoring, risk
stratification, among others. Although the data generated from the
methods of this invention is suited for manual review and analysis,
in a preferred embodiment, data processing using high-speed
computers is utilized.
[0103] The invention also provides for the storage and retrieval of
a collection of profiles and comparisons in a computer data storage
apparatus, which can include, for example, magnetic disks, optical
disks, magneto-optical disks, DRAM, SRAM, SGRAM, SDRAM, RDRAM, DDR
RAM, magnetic bubble memory devices, and other data storage
devices, including CPU registers and on-CPU data storage
arrays.
[0104] This invention also preferably provides a magnetic disk,
such as an IBM-compatible (DOS, Windows, Windows 95/98/2000,
Windows NT, OS/2, etc.) or other format, e.g., Linux, SunOS,
Solaris, AIX, SCO, Unix, VMS, MV, Mactinosh etc., floppy diskette
or hard (fixed, Winchester) disk drive, comprising a bit pattern
encoding data collected from the methods of the present invention
in a file format suitable for retrievable and processing in a
computerized comparison or relative quantification method.
[0105] The invention also provides a network, comprising a
plurality of computing devices linked via a data link, such as an
Ethernet cable (coax or 10BaseT), telephone line, ISDN line,
wireless network, optical fiber, or other suitable signal
transmission medium, whereby at least one network device comprises
a pattern of magnetic domains and/or charge domains comprising a
bit pattern encoding data acquired from the methods of the
invention.
[0106] The invention also provides a method for transmitting data
that includes generating an electronic signal on an electronic
communications device, such as a modem, ISDN terminal adapter, DSL,
cable modem, ATM switch, or the like, wherein the signal includes
(in native or encrypted format) a bit pattern encoding data
collected using the methods of the present invention.
[0107] In one embodiment, the invention provides a computer system
for performing methods of the present invention. A central
processor is preferably initialized to load and execute the
computer program for alignment and/or comparison of results. Data
is entered into the central processor via an I/O device. Execution
of the computer program results in the central processor retrieving
the data from the data file. The target data or record and the
computer program can be transferred to secondary memory, which is
typically random access memory. For example, a central processor
can be a conventional computer; a program can be a commercial or
public domain molecular biology software package; a data file can
be an optical or magnetic disk, a data server, or a memory device;
an I/O device can be a terminal comprising a video display and a
keyboard, a modem, an ISDN terminal adapter, an Ethernet port, a
punched card reader, a magnetic strip reader, or other suitable I/O
device.
[0108] The invention also provides the use of a computer system,
such as that described above, which comprises, for example: (1) a
computer; (2) a stored bit pattern encoding a collection of
measurements obtained by the methods of the present invention,
which may be stored in the computer; (3) a comparison control; and
(4) a program for comparison.
[0109] This invention also provides kits for assessing cytoskeletal
protein and screening cellular samples for clinical parameters.
Such kits are useful, for example, for diagnostic or prognostic
tests. Kits can include a solution for stabilizing cells, i.e., a
fixative solution, labeling reagents for labeling cell types and/or
cytoskeletal protein. The kit can also include instructions to
detect and quantify the cytoskeletal protein in a sample, as well
as instructions to correlate the amount of cytoskeletal protein
detected with diagnostic and prognostic methods and/or screening
methods according to the present invention.
[0110] All publications and patent documents cited above are hereby
incorporated by reference in their entirety for all purposes to the
same extent as if each were so individually denoted.
[0111] The below examples are non-limiting and for illustrating the
present invention. Alternatives and variations of the below
examples within the scope of the present invention as per the below
claims may be carried out by a person skilled in the art.
EXAMPLES
Example 1
Measuring the Impact of Pathogens on the Actin Signature Using
T-Cells, Monocytes, and Neutrophils
[0112] Materials: Chemical toxins such as hexachlorobenzene,
chloropicrin, and cyclosporin A will be purchased from
Sigma-Aldrich Chemical Co. Appropriate CDC-approved suppliers of
ricin toxin, purified bacterial toxins, and influenza A virus
stocks will be utilized. Human leukocyte cell lines
Jurkat(T-cells), U937(monocytes), and HL60(neutrophils), and all
bacterial cultures will be obtained from ATCC or other CDC approved
source. Appropriate bacterial culture media will be purchased from
Difco Co., and equipment for anaerobic culture of Clostridium sp
will be obtained from Carolina Biologicals, Inc. Human cell culture
media appropriate for each leukocyte type will be obtained from
GIBCO-BRL Laboratories. Cell culture and bacterial culture supplies
will be obtained from Fisher Scientific Co. Blood samples will be
obtained from healthy donors.
[0113] The impact of specific pathogens on the cytoskeletal actin
signature of whole blood cultures will be assessed at increasing
concentration of pathogen (toxin concentration or multiplicity of
infection) to obtain dose response curve for each pathogen.
(Viability of cells will be determined for each culture condition
by trypan blue exclusion protocol.)
[0114] Experiments will be carried out to determine dose-response
curves of the toxic effects of each pathogen in whole blood
cultures. From stock solutions of purified toxins aliquots
representing increasing dose level in the pg/ml to ug/ml range will
be added to blood cultures in order to determine the effects of
increasing dose levels on the actin signature.
[0115] Suspensions of live bacteria, parasite, and virus of known
particle concentrations aliquots representing increasing MOI in the
range 10-10.sup.5 infectious units/ml will be added to blood
cultures, and signatures will be measured after 60 minutes of
pathogen exposure at 37.degree. C.
[0116] The time-course of response of whole blood cultures to
specific pathogens will be determined as measured by the evolution
of the actin Signature. (Viability of cells will be determined for
each culture condition by trypan blue exclusion protocol.)
[0117] Purified chemical and bacterial toxins will be put into
stock solutions at known concentration, and from these stocks the
toxins will be added to blood cultures. Blood cultures will be
exposed to various concentration of toxin, and aliquots of the
leukocyte cultures will be removed at periodic intervals (5 minutes
to 4 hours) for measurement of the actin signature. The evolution
of signatures will be analyzed over extended period of time (up to
2 weeks) by performing similar experiments using leukocyte cell
lines.
[0118] Suspensions of known particle concentrations of live
bacteria, parasite, and virus materials will be prepared, and from
these stocks the live organisms or infectious virus particles will
be added to whole blood cultures over a wide range of
multiplicities of infection (MOI) such as 10-10.sup.4 infectious
particles/ml. Aliquots of the leukocyte cultures will be removed at
periodic intervals (5 minutes to 4 hours) for determination of the
actin signature. The evolution of signatures will be analyzed over
extended period of time (up to 2 weeks) by performing similar
experiments using leukocyte cell lines.
[0119] The data generated by this technology consists of vectors in
a multidimensional parameter space of non-negative real numbers.
For each cell type, 6 parameters corresponding to the F-actin
level, Forward Scatter (a measure of cell size), and Side Scatter
(a measure of granularity), before and after receptor-mediated
stimulation will be measured. Stimulation of cells with an
activator cocktail containing LPS, FMLP, and OKT3 will be used to
assess the ability of the cells to respond to stimulation. The
sensitivity and resolution of the technology will be improved by
increasing the number of cell types analyzed. Each additional cell
type will provide 6 new signature parameters in addition to the 18
parameters currently used. The enhanced resolution of the signature
will improve the ability to resolve signatures from different
pathogens.
[0120] The 18-dimensional vector currently generated defines the
signature of a blood sample. The basic premise of the technology is
that specific pathogens cause unique shifts in these signatures in
the 18-dimensional space, and that pathogens (or classes of
pathogens that act by similar mechanism of action) can be
identified by classifying signatures using statistical analysis and
feature recognition.
[0121] Predictive classification models will be developed which
maximize the probability of correctly classifying an unknown
sample. SAS statistical analysis software (SAS Institute, 1999)
will be the primary tool used for statistical analysis, graphing
and reporting. Data exploration using scatter plots, summary
statistics, and other descriptive tools will be used to understand
the data and characterize any systematic variation caused by donor
characteristics such as age, gender, and race.
[0122] The statistical model used in this investigation is the
Multinomial Logistic Regression (MLR) (Hosmer et. al., (2000)
Applied Logistic Regression, Wiley.) implemented by the SAS
procedures GENMOD or LOGISTIC which allows the representation and
analysis of complex models incorporating discrete as well as
continuous independent variables. Repeated measurements on the same
sample will be used to account for repeated sample variability,
e.g., small temperature effects that will be accounted for by use
of General Estimating Equations which are implemented by the
"REPEATED" statement of GENMOD.
[0123] An efficient MLR model will be developed which will reduce
the probability of misclassification and maximize the probability
of correct classification. The strategy used for this purpose is to
first assemble training sets of data from healthy donor blood
samples exposed to specific pathogens. The MLR model will be
developed using GENMOD or LOGISTIC and the training data sets. The
resultant "df" will be tested against calibration data (signatures)
whose classification in terms of normal, or exposed to a specific
pathogen, is known. Performance of the model against the new
signature will be used to further refine the model through an
iterative process until the model gives acceptable results in terms
of correct classification with respect to the calibration data set.
A number of important variables such as pathogen concentration and
exposure time will be fully examined.
[0124] The best subset of parameter features that have predictive
power for recognition of exposure to pathogens will thus be
determined.
[0125] In the presence of secondary infections, additional analysis
will be required to resolve complex signatures for identification
of pathogen exposure. For example, secondary bacterial infection
following influenza exposure will result in a signature that is
more complex than infection by one pathogen alone. These complex
signatures can be resolved by mathematical modeling of signatures
from singular infections. Mathematical models are routinely used to
resolve complex signals in physical systems, and these models will
be employed to develop methods for resolving signatures from
secondary infection. For example, signatures from secondary
infections can be expressed as a function of singular infection
signatures, and regression analysis may be used to identify the
infection agents. Co-infection studies will be performed using
NIAID priority pathogens to develop models for resolving complex
signatures resulting from multiple infections.
[0126] Co-Infection Studies for Resolution of Complex
Signatures
[0127] Whole blood cultures will be infected with combinations of
toxins and pathogens to obtain complex signatures similar to those
that will be present in secondary infections. Combinatorial
mathematical models will be used to evaluate actin signatures for
identifying features that are representative of multiple exposures
and identification of agents. Blind studies will be performed to
assess the reliability of the models for accurate detection of
exposure to multiple agents.
[0128] The same techniques will be performed for other cytoskeletal
protein for example, intermediate filaments, microtubules,
spectrin, talin, vinculin, desmin, senaptin, vimentin, ezrin,
moesin, filamin, phakinin, actinin, profilin, fibrin, keratin,
myosin, dynein, and kinesin.
* * * * *